Datasets:
Tasks:
Object Detection
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Update parquet files
Browse files- README.dataset.txt +0 -16
- README.md +0 -83
- README.roboflow.txt +0 -16
- excavator-detector.py +0 -152
- data/valid.zip → full/excavator-detector-test.parquet +2 -2
- data/train.zip → full/excavator-detector-train.parquet +2 -2
- data/test.zip → full/excavator-detector-validation.parquet +2 -2
- thumbnail.jpg → mini/excavator-detector-test.parquet +2 -2
- data/valid-mini.zip → mini/excavator-detector-train.parquet +2 -2
- mini/excavator-detector-validation.parquet +3 -0
- split_name_to_num_samples.json +0 -1
README.dataset.txt
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# undefined > raw-images_640by640
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https://public.roboflow.ai/object-detection/undefined
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Provided by undefined
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License: CC BY 4.0
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This project is trying to create an efficient computer or machine vision model to detect different kinds of construction equipment in construction sites and we are starting with **three classes which are excavators, trucks, and wheel loaders.**
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The **dataset is provided by [Mohamed Sabek](https://www.linkedin.com/in/mohammadsabek/)**, a Spring 2022 Master of Science graduate from Arizona State University in [Construction Management and Technology](https://graduate.engineering.asu.edu/construction-management/).
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The raw images (v1) contains:
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1. 1,532 annotated examples of "excavators"
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2. 1,269 annotated examples of "dump truck"
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3. 1,080 annotated examples of "wheel loader"
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**Note:** versions 2 and 3 (v2 and v3) contain the raw images resized at 416 by 416 (stretch to) and 640 by 640 (stretch to) without any augmentations.
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README.md
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---
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task_categories:
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- object-detection
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tags:
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- roboflow
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- roboflow2huggingface
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- Manufacturing
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- Construction
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- Machinery
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---
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<div align="center">
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<img width="640" alt="keremberke/excavator-detector" src="https://huggingface.co/datasets/keremberke/excavator-detector/resolve/main/thumbnail.jpg">
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</div>
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### Dataset Labels
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```
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['excavators', 'dump truck', 'wheel loader']
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```
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### Number of Images
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```json
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{'test': 144, 'train': 2245, 'valid': 267}
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```
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### How to Use
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- Install [datasets](https://pypi.org/project/datasets/):
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```bash
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pip install datasets
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```
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- Load the dataset:
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```python
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from datasets import load_dataset
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ds = load_dataset("keremberke/excavator-detector", name="full")
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example = ds['train'][0]
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```
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### Roboflow Dataset Page
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[https://universe.roboflow.com/mohamed-sabek-6zmr6/excavators-cwlh0/dataset/3](https://universe.roboflow.com/mohamed-sabek-6zmr6/excavators-cwlh0/dataset/3?ref=roboflow2huggingface)
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### Citation
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```
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@misc{ excavators-cwlh0_dataset,
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title = { Excavators Dataset },
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type = { Open Source Dataset },
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author = { Mohamed Sabek },
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howpublished = { \\url{ https://universe.roboflow.com/mohamed-sabek-6zmr6/excavators-cwlh0 } },
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url = { https://universe.roboflow.com/mohamed-sabek-6zmr6/excavators-cwlh0 },
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journal = { Roboflow Universe },
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publisher = { Roboflow },
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year = { 2022 },
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month = { nov },
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note = { visited on 2023-01-16 },
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}
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```
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### License
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CC BY 4.0
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### Dataset Summary
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This dataset was exported via roboflow.ai on April 4, 2022 at 8:56 AM GMT
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It includes 2656 images.
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Excavator are annotated in COCO format.
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The following pre-processing was applied to each image:
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* Auto-orientation of pixel data (with EXIF-orientation stripping)
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* Resize to 640x640 (Stretch)
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No image augmentation techniques were applied.
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README.roboflow.txt
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Excavators - v3 raw-images_640by640
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==============================
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This dataset was exported via roboflow.ai on April 4, 2022 at 8:56 AM GMT
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It includes 2656 images.
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Excavator are annotated in COCO format.
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The following pre-processing was applied to each image:
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* Auto-orientation of pixel data (with EXIF-orientation stripping)
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* Resize to 640x640 (Stretch)
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No image augmentation techniques were applied.
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excavator-detector.py
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import collections
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import json
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import os
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import datasets
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_HOMEPAGE = "https://universe.roboflow.com/mohamed-sabek-6zmr6/excavators-cwlh0/dataset/3"
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_LICENSE = "CC BY 4.0"
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_CITATION = """\
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@misc{ excavators-cwlh0_dataset,
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title = { Excavators Dataset },
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type = { Open Source Dataset },
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author = { Mohamed Sabek },
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howpublished = { \\url{ https://universe.roboflow.com/mohamed-sabek-6zmr6/excavators-cwlh0 } },
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url = { https://universe.roboflow.com/mohamed-sabek-6zmr6/excavators-cwlh0 },
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journal = { Roboflow Universe },
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publisher = { Roboflow },
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year = { 2022 },
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month = { nov },
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note = { visited on 2023-01-16 },
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}
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"""
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_CATEGORIES = ['excavators', 'dump truck', 'wheel loader']
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_ANNOTATION_FILENAME = "_annotations.coco.json"
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class EXCAVATORDETECTORConfig(datasets.BuilderConfig):
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"""Builder Config for excavator-detector"""
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def __init__(self, data_urls, **kwargs):
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"""
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BuilderConfig for excavator-detector.
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Args:
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data_urls: `dict`, name to url to download the zip file from.
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**kwargs: keyword arguments forwarded to super.
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"""
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super(EXCAVATORDETECTORConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
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self.data_urls = data_urls
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class EXCAVATORDETECTOR(datasets.GeneratorBasedBuilder):
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"""excavator-detector object detection dataset"""
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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EXCAVATORDETECTORConfig(
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name="full",
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description="Full version of excavator-detector dataset.",
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data_urls={
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"train": "https://huggingface.co/datasets/keremberke/excavator-detector/resolve/main/data/train.zip",
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"validation": "https://huggingface.co/datasets/keremberke/excavator-detector/resolve/main/data/valid.zip",
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"test": "https://huggingface.co/datasets/keremberke/excavator-detector/resolve/main/data/test.zip",
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},
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),
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EXCAVATORDETECTORConfig(
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name="mini",
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description="Mini version of excavator-detector dataset.",
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data_urls={
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"train": "https://huggingface.co/datasets/keremberke/excavator-detector/resolve/main/data/valid-mini.zip",
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"validation": "https://huggingface.co/datasets/keremberke/excavator-detector/resolve/main/data/valid-mini.zip",
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"test": "https://huggingface.co/datasets/keremberke/excavator-detector/resolve/main/data/valid-mini.zip",
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},
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)
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]
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def _info(self):
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features = datasets.Features(
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{
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"image_id": datasets.Value("int64"),
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"image": datasets.Image(),
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"width": datasets.Value("int32"),
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"height": datasets.Value("int32"),
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"objects": datasets.Sequence(
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{
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"id": datasets.Value("int64"),
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"area": datasets.Value("int64"),
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"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
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"category": datasets.ClassLabel(names=_CATEGORIES),
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}
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),
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}
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)
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return datasets.DatasetInfo(
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features=features,
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homepage=_HOMEPAGE,
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citation=_CITATION,
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license=_LICENSE,
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)
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def _split_generators(self, dl_manager):
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data_files = dl_manager.download_and_extract(self.config.data_urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"folder_dir": data_files["train"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"folder_dir": data_files["validation"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"folder_dir": data_files["test"],
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},
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),
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]
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def _generate_examples(self, folder_dir):
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def process_annot(annot, category_id_to_category):
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return {
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"id": annot["id"],
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"area": annot["area"],
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"bbox": annot["bbox"],
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"category": category_id_to_category[annot["category_id"]],
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}
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image_id_to_image = {}
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idx = 0
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annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
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with open(annotation_filepath, "r") as f:
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annotations = json.load(f)
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category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
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image_id_to_annotations = collections.defaultdict(list)
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for annot in annotations["annotations"]:
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image_id_to_annotations[annot["image_id"]].append(annot)
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filename_to_image = {image["file_name"]: image for image in annotations["images"]}
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for filename in os.listdir(folder_dir):
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filepath = os.path.join(folder_dir, filename)
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if filename in filename_to_image:
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image = filename_to_image[filename]
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objects = [
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process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
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]
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with open(filepath, "rb") as f:
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image_bytes = f.read()
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yield idx, {
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"image_id": image["id"],
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"image": {"path": filepath, "bytes": image_bytes},
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"width": image["width"],
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"height": image["height"],
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"objects": objects,
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}
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idx += 1
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data/valid.zip → full/excavator-detector-test.parquet
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data/train.zip → full/excavator-detector-train.parquet
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size 164190371
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data/test.zip → full/excavator-detector-validation.parquet
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thumbnail.jpg → mini/excavator-detector-test.parquet
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+
oid sha256:ed1b85a4d8368a9d8ac3a3c4f6284d1d531bc46fbd6f0ef8917154cadec933f2
|
3 |
+
size 166025
|
data/valid-mini.zip → mini/excavator-detector-train.parquet
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ed1b85a4d8368a9d8ac3a3c4f6284d1d531bc46fbd6f0ef8917154cadec933f2
|
3 |
+
size 166025
|
mini/excavator-detector-validation.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ed1b85a4d8368a9d8ac3a3c4f6284d1d531bc46fbd6f0ef8917154cadec933f2
|
3 |
+
size 166025
|
split_name_to_num_samples.json
DELETED
@@ -1 +0,0 @@
|
|
1 |
-
{"test": 144, "train": 2245, "valid": 267}
|
|
|
|